OpenRaiser/Intern-Atlas huggingface dataset:
Designing a Hair-Clip Inspired Bistable Mechanism for Soft Fish Robots
Venue: arXiv cs.RO 2023
Year: 2023

The Hair clip mechanism (HCM) is an in-plane prestressed bistable mechanism proposed in our previous research [1]~[5] to enhance the functionality of soft robotics. HCMs have several advantages, such as high rigidity, high mobility, good repeatability, and design and fabrication simplicity, compared to existing soft and compliant robotics. Using our experience with fish robots, this work delves into designing a novel HCM robotic propulsion system made from PETG plastic, carbon fiber-reinforced plastic (CFRP), and steel. Detailed derivation and verification of the HCM theory are given, and the influence of key parameters like dimensions, material types, and servo motor specifications are summarized. The designing algorithm offers insight into HCM robotics. It enables us to search for suitable components, operate robots at a desired frequency, and achieve high-frequency and high-speed undulatory swimming for fish robots.
Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet
Venue: arXiv cs.RO 2023
Year: 2023

The integration of unmanned aerial vehicles (UAVs) into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defense. A critical prerequisite for this integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for UAVs. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in UAVs, facilitating safe and efficient BVLOS operations.
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
Venue: arXiv cs.CL 2023
Year: 2023

Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using length-limited encoders. However, these methods often struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level (e.g. topic or category). We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. Our method first introduces an unsupervised variational autoencoder (VAE) to extract latent topic vectors of context sentences. This approach not only allows the encoder to handle longer documents more effectively, conserves valuable input space, but also keeps a topic-level coherence. Additionally, we incorporate an external category memory, enabling the system to retrieve relevant categories for undecided mentions. By employing step-by-step entity decisions, this design facilitates the modeling of entity-entity interactions, thereby maintaining maximum coherence at the category level. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points. Our model demonstrates particularly outstanding performance on challenging long-text scenarios.
On Finding Optimal (Dynamic) Arborescences
Venue: arXiv cs.DS 2023
Year: 2023

Let G = (V, E) be a directed and weighted graph with vertex set V of size n and edge set E of size m, such that each edge (u, v) \in E has a real-valued weight w(u, c). An arborescence in G is a subgraph T = (V, E') such that for a vertex u \in V, the root, there is a unique path in T from u to any other vertex v \in V. The weight of T is the sum of the weights of its edges. In this paper, given G, we are interested in finding an arborescence in G with minimum weight, i.e., an optimal arborescence. Furthermore, when G is subject to changes, namely edge insertions and deletions, we are interested in efficiently maintaining a dynamic arborescence in G. This is a well known problem with applications in several domains such as network design optimization and in phylogenetic inference. In this paper we revisit algorithmic ideas proposed by several authors for this problem, we provide detailed pseudo-code as well as implementation details, and we present experimental results on large scale-free networks and on phylogenetic inference. Our implementation is publicly available at \url{https://gitlab.com/espadas/optimal-arborescences}.
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. Despite their potential, LLMs are known to harbor inherent biases which may negatively impact learners. Previous studies have investigated bias in models and data representations separately, neglecting the potential impact of LLM bias on human writing. In this paper, we investigate how bias transfers through an AI writing support pipeline. We conduct a large-scale user study with 231 students writing business case peer reviews in German. Students are divided into five groups with different levels of writing support: one classroom group with feature-based suggestions and four groups recruited from Prolific -- a control group with no assistance, two groups with suggestions from fine-tuned GPT-2 and GPT-3 models, and one group with suggestions from pre-trained GPT-3.5. Using GenBit gender bias analysis, Word Embedding Association Tests (WEAT), and Sentence Embedding Association Test (SEAT) we evaluate the gender bias at various stages of the pipeline: in model embeddings, in suggestions generated by the models, and in reviews written by students. Our results demonstrate that there is no significant difference in gender bias between the resulting peer reviews of groups with and without LLM suggestions. Our research is therefore optimistic about the use of AI writing support in the classroom, showcasing a context where bias in LLMs does not transfer to students' responses.
A Robust Bi-Directional Algorithm For People Count In Crowded Areas
Venue: arXiv cs.CV 2023
Year: 2023

People counting system in crowded places has become a very useful practical application that can be accomplished in various ways which include many traditional methods using sensors. Examining the case of real time scenarios, the algorithm espoused should be steadfast and accurate. People counting algorithm presented in this paper, is centered on blob assessment, devoted to yield the count of the people through a path along with the direction of traversal. The system depicted is often ensconced at the entrance of a building so that the unmitigated frequency of visitors can be recorded. The core premise of this work is to extricate count of people inflow and outflow pertaining to a particular area. The tot-up achieved can be exploited for purpose of statistics in the circumstances of any calamity occurrence in that zone. Relying upon the count totaled, the population in that vicinity can be assimilated in order to take on relevant measures to rescue the people.
Exploitation-Guided Exploration for Semantic Embodied Navigation
Venue: arXiv cs.CV 2023
Year: 2023

In the recent progress in embodied navigation and sim-to-robot transfer, modular policies have emerged as a de facto framework. However, there is more to compositionality beyond the decomposition of the learning load into modular components. In this work, we investigate a principled way to syntactically combine these components. Particularly, we propose Exploitation-Guided Exploration (XGX) where separate modules for exploration and exploitation come together in a novel and intuitive manner. We configure the exploitation module to take over in the deterministic final steps of navigation i.e. when the goal becomes visible. Crucially, an exploitation module teacher-forces the exploration module and continues driving an overridden policy optimization. XGX, with effective decomposition and novel guidance, improves the state-of-the-art performance on the challenging object navigation task from 70% to 73%. Along with better accuracy, through targeted analysis, we show that XGX is also more efficient at goal-conditioned exploration. Finally, we show sim-to-real transfer to robot hardware and XGX performs over two-fold better than the best baseline from simulation benchmarking. Project page: xgxvisnav.github.io
Serious Games in Digital Gaming: A Comprehensive Review of Applications, Game Engines and Advancements
Venue: arXiv cs.SE 2023
Year: 2023

Serious games are defined as applied games that focus on the gamification of an experience (e.g., learning and training activities) and are not strictly for entertainment purposes. In recent years, serious games have become increasingly popular due to their ability to simultaneously educate and entertain users. In this review, we provide a comprehensive overview of the different types of digital games and expand on the serious games genre while focusing on its various applications. Furthermore, we present the most widely used game engines used in the game development industry and extend the Unity game machine advantages. Lastly, we conclude our research with a detailed comparison of the two most popular choices (Unreal and Unity engines) and their respective advantages and disadvantages while providing future suggestions for serious digital game development.
A Simple and Efficient Baseline for Data Attribution on Images
Venue: arXiv cs.CV 2023
Year: 2023

Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. These approaches therefore come at a high computational cost, are memory intensive, and are hard to scale to large models or datasets. In this work, we focus on a minimalist baseline, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. Our method is model-agnostic and scales easily to large datasets. We show results on CIFAR-10 and ImageNet, achieving strong performance that rivals or outperforms state-of-the-art approaches at a fraction of the compute or memory cost. Contrary to prior work, our results reinforce the intuition that a model's prediction on one image is most impacted by visually similar training samples. Our approach serves as a simple and efficient baseline for data attribution on images.
MFAAN: Unveiling Audio Deepfakes with a Multi-Feature Authenticity Network
Venue: arXiv cs.SD 2023
Year: 2023

In the contemporary digital age, the proliferation of deepfakes presents a formidable challenge to the sanctity of information dissemination. Audio deepfakes, in particular, can be deceptively realistic, posing significant risks in misinformation campaigns. To address this threat, we introduce the Multi-Feature Audio Authenticity Network (MFAAN), an advanced architecture tailored for the detection of fabricated audio content. MFAAN incorporates multiple parallel paths designed to harness the strengths of different audio representations, including Mel-frequency cepstral coefficients (MFCC), linear-frequency cepstral coefficients (LFCC), and Chroma Short Time Fourier Transform (Chroma-STFT). By synergistically fusing these features, MFAAN achieves a nuanced understanding of audio content, facilitating robust differentiation between genuine and manipulated recordings. Preliminary evaluations of MFAAN on two benchmark datasets, 'In-the-Wild' Audio Deepfake Data and The Fake-or-Real Dataset, demonstrate its superior performance, achieving accuracies of 98.93% and 94.47% respectively. Such results not only underscore the efficacy of MFAAN but also highlight its potential as a pivotal tool in the ongoing battle against deepfake audio content.
The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
Venue: arXiv cs.LG 2023
Year: 2023

The pursuit of fairness in machine learning models has emerged as a critical research challenge in different applications ranging from bank loan approval to face detection. Despite the widespread adoption of artificial intelligence algorithms across various domains, concerns persist regarding the presence of biases and discrimination within these models. To address this pressing issue, this study introduces a novel method called "The Fairness Stitch (TFS)" to enhance fairness in deep learning models. This method combines model stitching and training jointly, while incorporating fairness constraints. In this research, we assess the effectiveness of our proposed method by conducting a comprehensive evaluation of two well-known datasets, CelebA and UTKFace. We systematically compare the performance of our approach with the existing baseline method. Our findings reveal a notable improvement in achieving a balanced trade-off between fairness and performance, highlighting the promising potential of our method to address bias-related challenges and foster equitable outcomes in machine learning models. This paper poses a challenge to the conventional wisdom of the effectiveness of the last layer in deep learning models for de-biasing.
Inclusive Portraits: Race-Aware Human-in-the-Loop Technology
Venue: arXiv cs.HC 2023
Year: 2023

AI has revolutionized the processing of various services, including the automatic facial verification of people. Automated approaches have demonstrated their speed and efficiency in verifying a large volume of faces, but they can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of "human-in-the-loop" (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers' individual characteristics and backgrounds. This paper proposes a new approach, called Inclusive Portraits (IP), that connects with social theories around race to design a racially-aware human-in-the-loop system. Our experiments have provided evidence that incorporating race into human-in-the-loop (HITL) systems for facial verification can significantly enhance performance, especially for services delivered to people of color. Our findings also highlight the importance of considering individual worker characteristics in the design of HITL systems, rather than treating workers as a homogenous group. Our research has significant design implications for developing AI-enhanced services that are more inclusive and equitable.
Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions
Venue: arXiv cs.IT 2023
Year: 2023

Reconfigurable intelligent surfaces (RIS) as an effective technique for intelligently manipulating channel paths through reflection to serve desired users. Full-duplex (FD) systems, enabling simultaneous transmission and reception from a base station (BS), offer the theoretical advantage of doubled spectrum efficiency. However, the presence of strong self-interference (SI) in FD systems significantly degrades performance, which can be mitigated by leveraging the capabilities of RIS. In this work, we consider joint BS and RIS beamforming for maximizing the downlink (DL) transmission rate while guaranteeing uplink (UL) rate requirement. We propose an FD-RIS beamforming (FRIS) scheme by adopting penalty convex-concave programming. Simulation results demonstrate the UL/DL rate improvements achieved by considering various levels of imperfect CSI. The proposed FRIS scheme validates their effectiveness across different RIS deployments and RIS/BS configurations. FRIS has achieved the highest rate compared to the other approximation method, conventional beamforming techniques, HD systems, and deployment without RIS.
FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion
Venue: arXiv cs.CV 2023
Year: 2023

For 3D object detection, both camera and lidar have been demonstrated to be useful sensory devices for providing complementary information about the same scenery with data representations in different modalities, e.g., 2D RGB image vs 3D point cloud. An effective representation learning and fusion of such multi-modal sensor data is necessary and critical for better 3D object detection performance. To solve the problem, in this paper, we will introduce a novel vision transformer-based 3D object detection model, namely FusionViT. Different from the existing 3D object detection approaches, FusionViT is a pure-ViT based framework, which adopts a hierarchical architecture by extending the transformer model to embed both images and point clouds for effective representation learning. Such multi-modal data embedding representations will be further fused together via a fusion vision transformer model prior to feeding the learned features to the object detection head for both detection and localization of the 3D objects in the input scenery. To demonstrate the effectiveness of FusionViT, extensive experiments have been done on real-world traffic object detection benchmark datasets KITTI and Waymo Open. Notably, our FusionViT model can achieve state-of-the-art performance and outperforms not only the existing baseline methods that merely rely on camera images or lidar point clouds, but also the latest multi-modal image-point cloud deep fusion approaches.
Random Field Augmentations for Self-Supervised Representation Learning
Venue: arXiv cs.CV 2023
Year: 2023

Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations. Previous work has shown that applying diverse data augmentations is crucial to downstream performance, but augmentation techniques remain under-explored. In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning. These transformations generalize the well-established affine and color transformations (translation, rotation, color jitter, etc.) and greatly increase the space of augmentations by allowing transformation parameter values to vary from pixel to pixel. The parameters are treated as continuous functions of spatial coordinates, and modeled as independent Gaussian random fields. Empirical results show the effectiveness of the new transformations for self-supervised representation learning. Specifically, we achieve a 1.7% top-1 accuracy improvement over baseline on ImageNet downstream classification, and a 3.6% improvement on out-of-distribution iNaturalist downstream classification. However, due to the flexibility of the new transformations, learned representations are sensitive to hyperparameters. While mild transformations improve representations, we observe that strong transformations can degrade the structure of an image, indicating that balancing the diversity and strength of augmentations is important for improving generalization of learned representations.
Analysis of the User Perception of Chatbots in Education Using A Partial Least Squares Structural Equation Modeling Approach
Venue: arXiv cs.HC 2023
Year: 2023

The integration of Artificial Intelligence (AI) into education is a recent development, with chatbots emerging as a noteworthy addition to this transformative landscape. As online learning platforms rapidly advance, students need to adapt swiftly to excel in this dynamic environment. Consequently, understanding the acceptance of chatbots, particularly those employing Large Language Model (LLM) such as Chat Generative Pretrained Transformer (ChatGPT), Google Bard, and other interactive AI technologies, is of paramount importance. However, existing research on chatbots in education has overlooked key behavior-related aspects, such as Optimism, Innovativeness, Discomfort, Insecurity, Transparency, Ethics, Interaction, Engagement, and Accuracy, creating a significant literature gap. To address this gap, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the determinant of chatbots adoption in education among students, considering the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM). Utilizing a five-point Likert scale for data collection, we gathered a total of 185 responses, which were analyzed using R-Studio software. We established 12 hypotheses to achieve its objectives. The results showed that Optimism and Innovativeness are positively associated with Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Conversely, Discomfort and Insecurity negatively impact PEOU, with only Insecurity negatively affecting PU. These findings provide insights for future technology designers, elucidating critical user behavior factors influencing chatbots adoption and utilization in educational contexts.
Healthcare Security Breaches in the United States: Insights and their Socio-Technical Implications
Venue: arXiv cs.CY 2023
Year: 2023

This research examines the pivotal role of human behavior in the realm of healthcare data management, situated at the confluence of technological advancements and human conduct. An in-depth analysis of security breaches in the United States from 2009 to the present elucidates the dominance of human-induced security breaches. While technological weak points are certainly a concern, our study highlights that a significant proportion of breaches are precipitated by human errors and practices, thus pinpointing a conspicuous deficiency in training, awareness, and organizational architecture. In spite of stringent federal mandates, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act, breaches persist, emphasizing the indispensable role of human factors within this domain. Such oversights not only jeopardize patient data confidentiality but also undermine the foundational trust inherent in the healthcare infrastructure. By probing the socio-technical facets of healthcare security infringements, this article advocates for an integrated, dynamic, and holistic approach to healthcare data security. The findings underscore the imperative of augmenting technological defenses while concurrently elevating human conduct and institutional ethos, thereby cultivating a robust and impervious healthcare data management environment.
Faster Algorithms for Cycle Hitting Problems on Disk Graphs
Venue: arXiv cs.CG 2023
Year: 2023

In this paper, we consider three hitting problems on a disk intersection graph: Triangle Hitting Set, Feedback Vertex Set, and Odd Cycle Transversal. Given a disk intersection graph $G$, our goal is to compute a set of vertices hitting all triangles, all cycles, or all odd cycles, respectively. Our algorithms run in time $2^{\tilde O(k^{4/5})}n^{O(1)}$, $2^{\tilde O(k^{9/10})}n^{O(1)}$, and $2^{\tilde O(k^{19/20})}n^{O(1)}$, respectively, where $n$ denotes the number of vertices of $G$. These do not require a geometric representation of a disk graph. If a geometric representation of a disk graph is given as input, we can solve these problems more efficiently. In this way, we improve the algorithms for those three problem by Lokshtanov et al. [SODA 2022].